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Assessing socioeconomic effects on different sized populations: To weight or not to weight?
  1. N Frohlicha,
  2. K C Carriereb,
  3. L Potvinc,
  4. C Blacka
  1. aManitoba Centre for Health Policy and Evaluation, University of Manitoba, Canada, bDepartment of Mathematical Sciences, University of Alberta, Canada, cGRIS—Université de Montréal
  1. Professor Frohlich, Department of Business Administration, I.H. Asper School of Business, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V4 (Norman_Frohlich{at}


OBJECTIVE Researchers in health care often use ecological data from population aggregates of different sizes. This paper deals with a fundamental methodological issue relating to the use of such data. This study investigates the question of whether, in doing analyses involving different areas, the estimating equations should be weighted by the populations of those areas. It is argued that the correct answer to that question turns on some deep epistemological issues that have been little considered in the public health literature.

DESIGN To illustrate the issue, an example is presented that estimates entitlements to primary physician visits in Manitoba, Canada based on age/gender and socioeconomic status using both population weighted and unweighted regression analyses.

SETTING AND SUBJECTS The entire population of the province furnish the data. Primary care visits to physicians based on administrative data, demographics and a measure of socioeconomic status (SERI), based on census data, constitute the measures.

RESULTS Significant differences between weighted and unweighted analyses are shown to emerge, with the weighted analyses biasing entitlements towards the more populous and advantaged population.

CONCLUSIONS The authors endorse the position that, in certain problems, data analyses involving population aggregates unweighted by population size are more appropriate and normatively justifiable than are analyses weighted by population. In particular, when the aggregated units make sense, theoretically, as units, it is more appropriate to carry out the analyses without weighting by the size of the units. Unweighted analyses yield more valid estimations.

  • socioeconomic status
  • population weighting

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  • Conflicts of interest: none.

  • *  For example, the entitlement of a 4 year old girl from an area with a SERI of 1 would be calculated using the coefficients of the variables and their cross terms as follows: Graphic

  • * There are other ways in which population size could be entered into the analysis. One could conduct a variance weighted analysis. We also did that, but the analysis lead to similar conclusions and so is not reported here. Alternatively, population size in itself could be considered an independent variable which either directly, or indirectly, affects the outcome variable. So, for example, if physician supply were expected to be greater or transportation costs lower, in areas of high population, then utilisation rates might be dependent on the size of the area.